the major health challenges facing our country today. The Secretary's Advisory Committee on Healthy People Objectives for 2020 (Office of Disease Prevention and Health Promotion 2010) has further advised that all federal agencies (e.g. the Departments of Education, Transportation, and HUD) should be required to include Healthy People in their strategic plans.
In 2010, the US state of California created a HiAP Task Force, with representation of 19 state agencies, offices, and departments. Employing a HiAP framework, this statewide effort brought policymakers together to identify and recommend programs, policies, and strategies to improve health, including multiagency initiatives addressing transportation, housing, affordable healthy foods, safe neighborhoods, and green spaces. Additional recommendations included the development of health criteria in the discretionary funding review process and incorporating health issues into statewide data collection and survey efforts (Health in All Policies Task Force 2010).
The region of South Australia has also implemented the HiAP approach. Its HiAP model is based on the twin pillars of central governance and accountability and a “health lens” analysis process, which aims to identify key interactions and synergies between South Australia's Strategic Plan (SASP) targets, policies, and population health (Kickbusch and Buckett 2010). Notably, it was in Adelaide, the capital of South Australia, that the 2010 Adelaide Statement of HiAP was first developed. The South Australian Public Health Act was developed during the early implementation stages of HiAP and provided a legislative mandate to allow HiAP approaches to be systematically adopted across state and local governments within the region (Delany et al. 2015).
To strengthen the overall accountability for the HiAP pledges made by countries in the 2011 Rio Political Declaration on Social Determinants of Health, the WHO is currently developing a global monitoring system for intersectoral interventions on the social determinants of health to improve health equity (World Health Organization 2016b).
1.5 Conventional Approaches to Studying the Social Determinants of Health
Randomized experiments are the gold standard of study designs to establish cause‐and‐effect relationships. Yet, it is often neither feasible nor ethical to conduct experiments that randomly assign people or places to different levels of social determinants of health. As a result, evidence on the impacts of the social determinants of health has been largely based on observational studies, i.e. ecological, cohort, case–control, and cross‐sectional studies. Within such observational studies, traditional epidemiological approaches for studying the impacts of social determinants of health include multivariate analysis, which controls for factors that predict both the social determinants and health outcomes, i.e. so‐called potential “confounders.”
In addition, studies have explored these relationships by testing for single or multiple factors as potential mediators of the population health impacts of social determinants that could lend plausibility to the presence of causal associations. Because such social determinants are often contextual or area‐based factors (e.g. factors at the neighborhood or regional level), multilevel models that incorporate the hierarchical structure of data—such as individuals living within neighborhoods or states—are used to account for similarities and statistical nonindependence of individuals living within the same geographical areas (Goldstein et al. 2002).
1.6 Novel Approaches to Strengthen Causal Inference in Studying the Social Determinants of Health
A growing body of literature is attempting to reduce alternative explanations and other sources of bias in nonexperimental studies on the social determinants of health and more generally within public health. These novel approaches to strengthen causal inference include but are not limited to instrumental variable (IV) analysis, fixed effects analysis, propensity score analysis, inverse probability weighting, and natural experiments. By isolating random variation in the exposure, IV analysis can yield unbiased estimates of the causal association between an exposure and outcome, including through reducing attenuation bias due to measurement error and confounding bias due to both observed and unobserved factors (Kim 2016). Such approaches are increasingly being used to evaluate the causal roles of risk factors in public health, including obesity, neighborhood conditions, the social environment, and state policies (Davey Smith et al. 2009; Fish et al. 2010; Kim et al. 2011; Mojtabai and Crum 2013; Hawkins and Baum 2014; Kim 2016).
Similar to multivariable regression, propensity score analysis can control for imbalances between comparison groups and can thereby control for confounding. It has the advantage of being more efficient than traditional regression when there are relatively fewer events (Cepeda et al. 2003). However, like multivariable regression, propensity score analysis cannot control for unobserved or unmeasured confounders. Inverse probability weighting has also been used as an approach to estimate the counterfactual or potential outcome if all subjects were assigned to either exposure/treatment (Mansournia and Altman 2016). Finally, natural experiments or other quasi‐experimental designs such as regression discontinuity designs (Moscoe et al. 2015) can exploit random variation in exposures as in an experimental study and can thereby minimize confounding due to both observed and unobserved factors as a source of bias.
Results from individual studies can also be qualitatively reviewed in aggregate to identify existing gaps in methodological approaches, potential sources of bias, and similarities/differences in their results. Results across studies can be quantitatively summarized in meta‐analyses that yield overall point estimates of exposure–outcome associations, although, importantly, such estimates are only as good as the quality of the studies that are included in the meta‐analyses (Egger et al. 2001).
1.7 What Do We Know About the Social Determinants of Health?
As Bambra et al. (2010) have noted, there are clear limitations to the existing evidence based on the social determinants of health. First, observational studies that dominate the literature can only hint at possible interventions and their associated health effects; causal inference is an inherent limitation. Second, there is still only sparse evidence on the impacts of interventions on the social determinants of health. Bambra et al. (2010) conducted an “umbrella review” of the existing systematic reviews of the evidence on specific interventions on the social determinants of health spanning housing/living environment, work environment, transportation, health and social care services, agriculture and food, and water and sanitation. They identified some suggestive evidence that certain categories of interventions may impact inequalities regarding the health of specific disadvantaged groups, particularly in the fields of housing and work environment. Yet in other areas, such as evidence on policies in education, the health system, food and agriculture, and more generally on the influences of macro‐level policies on health inequalities, the empirical literature on interventions was more limited (Bambra et al. 2010).
In a more recent umbrella review, Thomson et al. (2017) adopted a systematic review approach to summarize the state of knowledge on how public health policy interventions (e.g. taxation and educational campaigns) may impact health inequalities such as differential effects across socioeconomic groups or effects of interventions targeted at disadvantaged groups. After searching studies published up to May 2017 within 20 databases (e.g. Medline, EMBASE, CINAHL, PsycINFO, Social Science Citation Index, Sociological Abstracts, and the Cochrane Library), the authors identified 24 systematic reviews reporting 128 relevant primary studies. They then summarized the evidence on policies (fiscal, regulation, education, preventive treatment, and screening) across eight public health domains (tobacco; food and nutrition; the control of infectious diseases; screening; road traffic injuries; air, land, and water pollution; built environment; and workplace regulations). The systematic reviews were mixed in quality, and the results were mixed across public health domains. For the tobacco, food and nutrition, and control of infectious diseases domains, the authors found evidence to suggest that fiscal and regulation policies were more beneficial for reducing or preventing health inequalities than educational campaigns (Thomson et al. 2017).
1.8
